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ORCIDJohn M. Winn
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- affiliation: Microsoft Research Cambridge
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2020 – today
- 2021
[c33]John M. Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise J. Wilkins, Siân E. Lindley, Richard Banks, Sam Webster, Yordan Zaykov:
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities. AKBC 2021- 2020
- [j13]Lukasz Romaszko, Christopher K. I. Williams, John M. Winn:
Learning Direct Optimization for scene understanding. Pattern Recognit. 105: 107369 (2020)
2010 – 2019
- 2019
- [c32]John M. Winn, John Guiver, Sam Webster, Yordan Zaykov, Martin Kukla, Dany Fabian:
Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program. AKBC 2019 - 2018
- [i4]Lukasz Romaszko, Christopher K. I. Williams, John M. Winn:
Learning Direct Optimization for Scene Understanding. CoRR abs/1812.07524 (2018) - 2016
- [i3]Liwen Zhang, John M. Winn, Ryota Tomioka:
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. CoRR abs/1611.02266 (2016) - 2015
- [j12]Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman:
The Pascal Visual Object Classes Challenge: A Retrospective. Int. J. Comput. Vis. 111(1): 98-136 (2015) - [c31]Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn:
Consensus Message Passing for Layered Graphical Models. AISTATS 2015 - 2014
- [j11]S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, John M. Winn:
The Shape Boltzmann Machine: A Strong Model of Object Shape. Int. J. Comput. Vis. 107(2): 155-176 (2014) - [c30]S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn:
Just-In-Time Learning for Fast and Flexible Inference. NIPS 2014: 154-162 - [i2]Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn:
Consensus Message Passing for Layered Graphical Models. CoRR abs/1410.7452 (2014) - 2013
- [c29]Nevena Lazic, Christopher M. Bishop, John M. Winn:
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. AISTATS 2013: 379-387 - [c28]Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John M. Winn, Antonio Criminisi:
Decision Jungles: Compact and Rich Models for Classification. NIPS 2013: 234-242 - [c27]Nicolas Heess, Daniel Tarlow, John M. Winn:
Learning to Pass Expectation Propagation Messages. NIPS 2013: 3219-3227 - 2012
- [j10]Theofanis Karaletsos, Oliver Stegle
, Christine Dreyer, John M. Winn, Karsten M. Borgwardt:
ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinform. 28(7): 1001-1008 (2012) - [j9]Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc Van Gool, Josef Sivic, Christopher K. I. Williams, David C. Hogg:
In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. 34(11): 2081-2082 (2012) - [c26]S. M. Ali Eslami, Nicolas Heess, John M. Winn:
The Shape Boltzmann Machine: A strong model of object shape. CVPR 2012: 406-413 - [c25]John M. Winn:
Causality with Gates. AISTATS 2012: 1314-1322 - 2011
- [j8]Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn:
Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Comput. 23(3): 593-650 (2011) - [j7]Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa:
Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 33(1): 30-42 (2011) - [c24]Nicolas Heess, Nicolas Le Roux, John M. Winn:
Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) 2011: 9-16 - [c23]Albert Montillo, Jamie Shotton, John M. Winn, Juan Eugenio Iglesias, Dimitris N. Metaxas, Antonio Criminisi:
Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. IPMI 2011: 184-196 - [i1]Nicolas Heess, Nicolas Le Roux, John M. Winn:
Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs. CoRR abs/1107.3823 (2011) - 2010
- [j6]Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman:
The Pascal Visual Object Classes (VOC) Challenge. Int. J. Comput. Vis. 88(2): 303-338 (2010) - [j5]Oliver Stegle, Leopold Parts, Richard Durbin, John M. Winn:
A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies. PLoS Comput. Biol. 6(5) (2010)
2000 – 2009
- 2009
- [j4]Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi:
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. Int. J. Comput. Vis. 81(1): 2-23 (2009) - [j3]Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn:
Epitomic Location Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2158-2167 (2009) - [p1]Iain E. Buchan, John M. Winn, Christopher M. Bishop:
A unified modeling approach to data-intensive healthcare. The Fourth Paradigm 2009: 91-97 - 2008
- [c22]Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn:
Epitomic location recognition. CVPR 2008 - [c21]Vincent Y. F. Tan, John M. Winn, Angela Simpson, Adnan Custovic:
Immune System Modeling with Infer.NET. eScience 2008: 364-365 - [c20]Tom Minka, John M. Winn:
Gates. NIPS 2008: 1073-1080 - [c19]Oliver Stegle, Anitha Kannan, Richard Durbin, John M. Winn:
Accounting for Non-genetic Factors Improves the Power of eQTL Studies. RECOMB 2008: 411-422 - 2007
- [j2]Jean-François Lalonde, Derek Hoiem, Alexei A. Efros, Carsten Rother, John M. Winn, Antonio Criminisi:
Photo clip art. ACM Trans. Graph. 26(3): 3 (2007) - [c18]Thomas Deselaers, Antonio Criminisi, John M. Winn, Ankur Agarwal:
Incorporating On-demand Stereo for Real Time Recognition. CVPR 2007 - [c17]Derek Hoiem, Carsten Rother, John M. Winn:
3D LayoutCRF for Multi-View Object Class Recognition and Segmentation. CVPR 2007 - [c16]Julia A. Lasserre, Anitha Kannan, John M. Winn:
Hybrid learning of large jigsaws. CVPR 2007 - [c15]Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa:
Tree-based Classifiers for Bilayer Video Segmentation. CVPR 2007 - [c14]Jim C. Huang, Anitha Kannan, John M. Winn:
Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. ISMB/ECCB (Supplement of Bioinformatics) 2007: 212-221 - [c13]Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John M. Winn, Andrew Blake, Andrew W. Fitzgibbon:
C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces. Tabletop 2007: 3-10 - 2006
- [c12]John M. Winn, Jamie Shotton:
The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects. CVPR (1) 2006: 37-44 - [c11]Nebojsa Jojic, John M. Winn, C. Lawrence Zitnick:
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video. CVPR (1) 2006: 117-124 - [c10]Silvio Savarese, John M. Winn, Antonio Criminisi:
Discriminative Object Class Models of Appearance and Shape by Correlatons. CVPR (2) 2006: 2033-2040 - [c9]Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi:
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. ECCV (1) 2006: 1-15 - [c8]Ashish Kapoor, John M. Winn:
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection. ECCV (3) 2006: 302-315 - [c7]Anitha Kannan, John M. Winn, Carsten Rother:
Clustering appearance and shape by learning jigsaws. NIPS 2006: 657-664 - 2005
- [j1]John M. Winn, Christopher M. Bishop:
Variational Message Passing. J. Mach. Learn. Res. 6: 661-694 (2005) - [c6]John M. Winn, Nebojsa Jojic:
LOCUS: Learning Object Classes with Unsupervised Segmentation. ICCV 2005: 756-763 - [c5]John M. Winn, Antonio Criminisi, Thomas P. Minka:
Object Categorization by Learned Universal Visual Dictionary. ICCV 2005: 1800-1807 - 2004
- [c4]John M. Winn, Andrew Blake:
Generative Affine Localisation and Tracking. NIPS 2004: 1505-1512 - 2003
- [c3]Christopher M. Bishop, John M. Winn:
Structured Variational Distributions in VIBES. AISTATS 2003: 33-40 - 2002
- [c2]Christopher M. Bishop, David J. Spiegelhalter, John M. Winn:
VIBES: A Variational Inference Engine for Bayesian Networks. NIPS 2002: 777-784 - 2000
- [c1]Christopher M. Bishop, John M. Winn:
Non-linear Bayesian Image Modelling. ECCV (1) 2000: 3-17
Coauthor Index
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